import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None





@experimental
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if ParallelBackendConfig.backend_name is None:
  return _map_with_multiprocessing_pool(
      UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 return _map_with_joblib(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      num_proc if num_proc <= len(UpperCamelCase_       ) else len(UpperCamelCase_       )
 UpperCamelCase			  =      []  # We organize the splits ourselve (contiguous splits)
 for index in range(UpperCamelCase_       ):
  UpperCamelCase			  =      len(UpperCamelCase_       ) // num_proc
  UpperCamelCase			  =      len(UpperCamelCase_       ) % num_proc
  UpperCamelCase			  =      div * index + min(UpperCamelCase_							,					UpperCamelCase_       )
  UpperCamelCase			  =      start + div + (1 if index < mod else 0)
  split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

 if len(UpperCamelCase_       ) != sum(len(i[1]       ) for i in split_kwds       ):
  raise ValueError(
      f"""Error dividing inputs iterable among processes. """
      f"""Total number of objects {len(UpperCamelCase_       )}, """
      f"""length: {sum(len(i[1]       ) for i in split_kwds       )}"""       )

 logger.info(
     f"""Spawning {num_proc} processes for {len(UpperCamelCase_       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}"""       )
 UpperCamelCase  ,     UpperCamelCase			  =      None, None
 if not disable_tqdm:
  UpperCamelCase  ,     UpperCamelCase			  =      (RLock(),), tqdm.set_lock
 with Pool(UpperCamelCase_							,					initargs=UpperCamelCase_							,					initializer=UpperCamelCase_       ) as pool:
  UpperCamelCase			  =      pool.map(UpperCamelCase_							,					UpperCamelCase_       )
 logger.info(f"""Finished {num_proc} processes"""       )
 UpperCamelCase			  =      [obj for proc_res in mapped for obj in proc_res]
 logger.info(f"""Unpacked {len(UpperCamelCase_       )} objects"""       )

 return mapped





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
 # and it requires monkey-patching joblib internal classes which is subject to change
 import joblib

 with joblib.parallel_backend(ParallelBackendConfig.backend_name							,					n_jobs=UpperCamelCase_       ):
  return joblib.Parallel()(
      joblib.delayed(UpperCamelCase_       )((function, obj, types, None, True, None)       ) for obj in iterable       )





@experimental
@contextlib.contextmanager
def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      backend_name

 if backend_name == "spark":
  from joblibspark import register_spark

  register_spark()

  # TODO: call create_cache_and_write_probe if "download" in steps
  # TODO: raise NotImplementedError when Dataset.map etc is called

 try:
  yield
 finally:
  UpperCamelCase			  =      None

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
